Work

4D spatial–temporal information infrastructure for digital twin environments using Spatial IDs

Year: 2025

Type: article

Abstract: Digital twin technologies are gaining global attention as foundational components for data-driven infrastructure management and urban simulation. However, significant challenges remain in integrating and utilizing geospatial datasets, including inconsistencies in spatial referencing methods, the absence of semantic object identification, and alignment issues due to positional inaccuracies. This study proposes a 4D spatial–temporal information infrastructure leveraging a voxel-based spatial referencing method known as Spatial IDs. The infrastructure enables unified spatial indexing across heterogeneous datasets and supports high-speed data retrieval, dynamic distribution, and automatic object identification from point cloud data. Key system components include a binary voxel-based search index, a data compression and distribution mechanism using LASzip format, and a semantic segmentation module that integrates map-based object annotations with point cloud geometry. Demonstration experiments involving city-scale digital twin construction, drone route interference detection, and object identification confirmed the infrastructure's efficiency and scalability. The proposed infrastructure reduced retrieval time to under one second and achieved high object identification accuracy (F1-score ≥ 0.99), illustrating its applicability in real-time geospatial analytics and intelligent urban systems.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1111/mice.70146

Authors: Kenji Nakamura, Yoshimasa Umehara, Masaya Nakahara, Ryuichi Imai

Institutions: Osaka University of Economics, Setsunan University, Osaka Electro-Communication University, Hosei University

Language: English

Topic: 3D Modeling in Geospatial Applications

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,230